Neural network establishes co-occurrence links between transformation products of the contaminant and the soil microbiome.

It remains challenging to establish reliable links between transformation products (TPs) of contaminants and corresponding microbes. This challenge arises due to the sophisticated experimental regime required for TP discovery and the compositional nature of 16S rRNA gene amplicon sequencing and mass spectrometry datasets, which can potentially confound statistical inference. In this study, we present a new strategy by combining the use of 2H-labeled Stable Isotope-Assisted Metabolomics (2H-SIAM) with a neural network-based algorithm (i.e., MMvec) to explore links between TPs of pyrene and the soil microbiome. The links established by this novel strategy were further validated using different approaches. Briefly, a metagenomic study provided indirect evidence for the established links, while the identification of pyrene degraders from soils, and a DNA-based stable isotope probing (DNA-SIP) study offered direct evidence. The comparison among different approaches, including Pearson's and Spearman's correlations, further confirmed the superior performance of our strategy. In conclusion, we summarize the unique features of the combined use of 2H-SIAM and MMvec. This study not only addresses the challenges in linking TPs to microbes but also introduces an innovative and effective approach for such investigations. Environmental Implication: Taxonomically diverse bacteria performing successive metabolic steps of the contaminant were firstly depicted in the environmental matrix.

Global Tracking of Transformation Products of Environmental Contaminants by 2H-Labeled Stable Isotope-Assisted Metabolomics.

Stable isotope-assisted metabolomics (SIAM) enables global tracking of isotopic labels in nontargeted metabolomics in living organisms. However, its application in tracking transformation products (TPs, as metabolites of contaminants) of environmental contaminants is still a challenge due to limits in methodology, unmatured algorithms, and the high cost of 13C-labeled contaminants. Therefore, we developed a 2H-SIAM pipeline coupled with a highly flexible algorithm 2H-SIAM(1.0) (https://github.com/kechen1984/2H-SIAM), facilitating tracking TPs of contaminants in the environmental matrix. A detailed discussion illustrates the theory, behavior, and prospect of 2H-SIAM. We demonstrate that the proposed 2H-SIAM pipeline has unique advantages over 13C-SIAM, for example, cost-effective 2H-labeled contaminants, easy synthesis of 2H-labeled emerging contaminants, and providing more structural information. A pyrene soil degradation study further confirmed its high performance. It efficiently discarded 99% of noise signals and extracted 52 features from the nontargeted high resolution mass spectrometry (HRMS) data. Among them, 13 features were annotated as TPs of pyrene with identification confidence from Level 2a to Level 5, and 5 TPs were reported for the first time. In conclusion, the proposed 2H-SIAM pipeline is powerful in tracking potential TPs of environmental contaminants with unique advantages.